For the paper, we analyzed harvested transaction data from the core processors of more than 1,000 branches across the U.S. and aggregated available payroll hours and salary/benefit costs for more than 10,000 financial services employees at these branches.

Our purpose was to identify and quantify the productivity discrepancy between high- and low-performing branches, thereby illustrating how much revenue financial institutions could recapture by closing that gap through measures such as proper scheduling.

Although the paper achieved our goal, the results of the study surprised even us.

The top 10 performing branches in terms of workforce utilization (WFU) had an average WFU percentage of more than 82%, while the bottom performers had an average WFU of less than 64%.

Workforce Utilization Terminology

Terms you will encounter in this and subsequent articles:

• Workforce utilization (WFU): A percentage achieved by dividing the total number of teller processing hours by their payroll hours.

• Processing hours: The time in which a teller performs at least one member-facing transaction, measured in 15-minute increments rather than payroll hours.

If a teller performs a transaction at 8:09, for example, and then does not process another transaction until 9:57, only 0.5 hours would qualify as processing hours, even though two payroll hours passed.

• Excess waiting for work time: Those periods when too many tellers are scheduled to work for the transaction flow coming through the branch (also referred as nonvolume time and idle time).

• Transactions per hour: Total transaction volume as reported by the core processor, divided by total number of processing hours.

• Labor cost per transaction: Average labor expense per transaction. This metric does not include overhead and other nonpayroll expenses in its calculation.

Because of auxiliary activities, no individual teller achieves 100% WFU (the FMSI benchmark is 75%). However, with a WFU of 64%, the bottom branches were spending 36% of their time on nonvolume, nonmember-facing activities.

Digging deeper

Once we determined these baseline numbers, we examined what tellers were doing while they were not processing transactions, transforming the branch from a profit center to a drain on profitability.

In some cases, low WFU percentages resulted from relying too heavily on tellers to perform administrative tasks. In others, it indicated that inadequately trained tellers were wasting transaction time by, for instance, leaving their stations to answer members’ questions.

In the least efficient scenarios, which almost universally correlated with overstaffing and/or low-volume branches, tellers were simply sitting idle while they waited for members to arrive.

Excess administrative time and inadequate training can be rectified through better personnel management. However, periods of lower productivity—what we call excess waiting for work time—is most effectively eliminated by optimizing staffing levels through a scheduling model using forecasted transaction volumes to align the precise mix of full- and part-time tellers.

In the examples we evaluated for the study, once this happened, excess waiting for work time and an important indicator of productivity, transactions per hour, rose in response.

Transactions per hour is a powerful metric that reveals how much work tellers accomplish during qualifying processing hours. It helps quantify the stark difference between poorly performing, inefficiently scheduled, and managed teller staffs, and those that are working at peak performance.

In the case of one financial institution in the study, when excess waiting for work time was reduced from 39% to 12.2% through effective scheduling, transactions per hour rose from 15.7 to 21.9.

A correlating metric, labor cost per transaction (which measures the cost of staff labor per transaction), dropped from 88 cents to 65 cents.

Putting the study to work

In this article, we introduced you to the FMSI WFU study and its terminology and showed how it identified serious, schedule-related inefficiencies—and accompanying revenue losses—within financial institutions.

In subsequent articles, we will explore how your branch can use similar data to calculate current operating metrics and use that information to improve productivity and increase revenue.